23 research outputs found
A Machine Learning Approach for Identifying Novel Cell Type–Specific Transcriptional Regulators of Myogenesis
Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA–based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression patterns
STRIDER NZAus: a multicentre randomised controlled trial of sildenafil therapy in early‐onset fetal growth restriction
Prenatal Sildenafil Therapy Improves Cardiovascular Function in Fetal Growth Restricted Offspring of Dahl Salt-Sensitive Rats
Influence of phytoestrogens on endometrial thickness: a systematic review and meta-analysis
Maternal sildenafil impairs the cardiovascular adaptations to chronic hypoxaemia in fetal sheep
Utero-placental perfusion Doppler indices in growth restricted fetuses: effect of sildenafil citrate
Addition of sildenafil citrate for treatment of severe intrauterine growth restriction: a double blind randomized placebo controlled trial
Developing a new algorithm for first and second trimester preeclampsia screening in twin pregnancies
Objectives: Construct a new preeclampsia predicting algorithm in twins. Methods: Twins sampled at 10–13 and 16–20 gestational weeks and their marker values were log transformed into multiples of the gestation-specific medians (MoMs) for singletons and entered into a new logistic regression model with/without prior risk factors. Results: The cohort included 9 PE (18 samples) and 96 unaffected cases (175 samples) twin pregnant women. The algorithm constructed of PlGF, PAPP-A, PP13, Doppler UTPI, and MAP with prior risk factors generated an area under the curve of 0.918, 75% detection rate for 10% false-positive rate. Conclusions: The algorithm effectively forecasted twin risk to develop PE